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Institution

Cornell University

EducationIthaca, New York, United States
About: Cornell University is a education organization based out in Ithaca, New York, United States. It is known for research contribution in the topics: Population & Gene. The organization has 102246 authors who have published 235546 publications receiving 12283673 citations. The organization is also known as: Cornell & CUI.


Papers
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Journal ArticleDOI
10 Apr 1992-Science
TL;DR: The macrophage enzyme is immunologically induced at the transcriptional level and closely resembles the enzyme in cytokine-treated tumor cells and inflammatory neutrophils.
Abstract: Nitric oxide (NO) conveys a variety of messages between cells, including signals for vasorelaxation, neurotransmission, and cytotoxicity. In some endothelial cells and neurons, a constitutive NO synthase is activated transiently by agonists that elevate intracellular calcium concentrations and promote the binding of calmodulin. In contrast, in macrophages, NO synthase activity appears slowly after exposure of the cells to cytokines and bacterial products, is sustained, and functions independently of calcium and calmodulin. A monospecific antibody was used to clone complementary DNA that encoded two isoforms of NO synthase from immunologically activated mouse macrophages. Liquid chromatography-mass spectrometry was used to confirm most of the amino acid sequence. Macrophage NO synthase differs extensively from cerebellar NO synthase. The macrophage enzyme is immunologically induced at the transcriptional level and closely resembles the enzyme in cytokine-treated tumor cells and inflammatory neutrophils.

1,890 citations

Journal ArticleDOI
19 Dec 2013-Cell
TL;DR: It is found that microglia could be specifically depleted from the brain upon diphtheria toxin administration and removal of brain-derived neurotrophic factor (BDNF) frommicroglia largely recapitulated the effects of microglian depletion.

1,890 citations

Journal ArticleDOI
01 Jul 1970-Ecology
TL;DR: The content of oak leaf tannins, which inhibit the growth of winter moth larvae, increases during the summer and may render leaves less suitable for insect growth by further reducing the availability of nitrogen and perhaps also by influencing leaf palatability.
Abstract: Concentration in the spring of feeding by caterpillars of the winter moth, Operophtera brumata L., and other species of Lepidoptera on oak trees in England is believed to be related to seasonal changes in the texture and chemical composition of the leaves. Increasing leaf toughness is a proximate, though probably not ultimate, factor preventing late larval feeding by the winter moth, the commonest spring species on oak. Early feeding coincides with maximum leaf protein content and mimum leaf sugar content, with suggests that availability of nitrogen, rather than of carbohydrate, may be a limiting factor for spring—feeding larvae. The content of oak leaf tannins, which inhibit the growth of winter moth larvae, increases during the summer and may render leaves less suitable for insect growth by further reducing the availability of nitrogen and perhaps also by influencing leaf palatability. Oak trees are extensively damaged by insect attack, and it is likely that leaf tannins have a defensive function against insects as well as against other herbivores and against pathogens.

1,885 citations

Posted Content
TL;DR: It is discovered that modern neural networks, unlike those from a decade ago, are poorly calibrated, and on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.

1,883 citations

Journal ArticleDOI
TL;DR: In this paper, a rigorous analysis of the annihilation decay rates of heavy quarkonium states is presented, with coefficients that can be computed using perturbation theory in non-relativistic QCD.
Abstract: A rigorous QCD analysis of the inclusive annihilation decay rates of heavy quarkonium states is presented. The effective-field-theory framework of nonrelativistic QCD is used to separate the short-distance scale of annihilation, which is set by the heavy quark mass M, from the longer-distance scales associated with quarkonium structure. The annihilation decay rates are expressed in terms of nonperturbative matrix elements of four-fermion operators in nonrelativistic QCD, with coefficients that can be computed using perturbation theory in the coupling constant ${\mathrm{\ensuremath{\alpha}}}_{\mathit{s}}$(M). The matrix elements are organized into a hierarchy according to their scaling with v, the typical velocity of the heavy quark. An analogous factorization formalism is developed for the production cross sections of heavy quarkonium in processes involving momentum transfers of order M or larger. The factorization formulas are applied to the annihilation decay rates and production cross sections of S-wave states at next-to-leading order in ${\mathit{v}}^{2}$ and P-wave states at leading order in ${\mathit{v}}^{2}$.

1,882 citations


Authors

Showing all 103081 results

NameH-indexPapersCitations
Eric S. Lander301826525976
David Miller2032573204840
Lewis C. Cantley196748169037
Charles A. Dinarello1901058139668
Scott M. Grundy187841231821
Paul G. Richardson1831533155912
Chris Sander178713233287
David R. Williams1782034138789
David L. Kaplan1771944146082
Kari Alitalo174817114231
Richard K. Wilson173463260000
George F. Koob171935112521
Avshalom Caspi170524113583
Derek R. Lovley16858295315
Stephen B. Baylin168548188934
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023309
20221,363
202112,457
202012,139
201910,787
20189,905